RHOAI
Deploy ROSA + Nvidia GPU + RHOAI with Automation
Getting Red Hat OpenShift AI up and running with NVIDIA GPUs on a Red Hat OpenShift Service on AWS (ROSA) cluster can involve a series of detailed steps, from installing various operators to managing dependencies. While manageable, this process can be time-consuming when you’re eager to start leveraging OpenShift AI for your projects.
This guide and its accompanying Git repository are designed to streamline your setup significantly. We focus on getting you productive faster by using Terraform to deploy a ROSA cluster with GPUs from the start. From there, Ansible scripts take over, automating the deployment and configuration of all necessary operators for both NVIDIA GPUs and Red Hat OpenShift AI. This means less manual configuration for you and more time spent on what matters: innovating with AI.
Building LLM Cost and Performance Dashboard with Red Hat OpenShift AI on ROSA and Amazon Bedrock
1. Introduction
As the LLM’s usage increases in the enterprise, not many realize that every LLM API call has two hidden costs: time and money. So while data scientists might argue about data accuracy, infrastructure engineers on the other hand, would need to know if that 2-second response time will scale, and if those $0.015 per thousand tokens cost will blow their quarterly budget, among others. In this guide, we will build a simple cost and performance dashboard for Amazon Bedrock models using Red Hat OpenShift AI (RHOAI) , which is our platform for managing AI/ML projects lifecycle, running on a Red Hat OpenShift Service on AWS (ROSA) cluster.
Creating Agentic AI to deploy ARO cluster using Terraform with Red Hat OpenShift AI on ROSA and Amazon Bedrock
1. Introduction
Agentic AI can be defined as systems that are capable of interpreting natural language instructions, in this case users’ prompts, making decisions based on those prompts, and then autonomously executing tasks on behalf of users. In this guide, we will create one that is intelligent enough that not only that it can understand/parse users’ prompts, but it can also take action upon it by deploying (and destroying) Azure Red Hat OpenShift (ARO) cluster using Terraform.
Creating RAG Chatbot using TinyLlama and LangChain with Red Hat OpenShift AI on ARO
1. Introduction
Retrieval-Augmented Generation (RAG) is a technique to enhance Large Language Models (LLMs) to retrieve relevant information from a knowledge base before generating responses, rather than relying solely on their training. LangChain is a framework for developing applications powered by language models. It provides tools and APIs that make it easier to create complex applications using LLMs, such as using RAG technique to enable the chatbot to answer questions based on the provided document.
Creating Images using Stable Diffusion on Red Hat OpenShift AI on ROSA cluster with GPU enabled
1. Introduction
Stable Diffusion is an AI model to generate images from text description. It uses a diffusion process to iteratively denoise random Gaussian noise into coherent images. This is a simple tutorial to create images using Stable Diffusion model using Red Hat OpenShift AI (RHOAI) , formerly called Red Hat OpenShift Data Science (RHODS), which is our OpenShift platform for AI/ML projects lifecycle management, running on a Red Hat OpenShift Services on AWS (ROSA) cluster, which is our managed service OpenShift platform on AWS, with NVIDIA GPU enabled.
Running and Deploying LLMs using Red Hat OpenShift AI on ROSA cluster and Storing the Model in Amazon S3 Bucket
1. Introduction
Large Language Models (LLMs) are a specific type of generative AI focused on processing and generating human language. They can understand, generate, and manipulate human language in response to various tasks and prompts.
This guide is a simple example on how to run and deploy LLMs on a Red Hat OpenShift Services on AWS (ROSA) cluster, which is our managed service OpenShift platform on AWS, using Red Hat OpenShift AI (RHOAI) , which is formerly called Red Hat OpenShift Data Science (RHODS) and is our OpenShift platform for managing the entire lifecycle of AI/ML projects. And we will utilize Amazon S3 bucket to store the model output. In essence, here we will first install RHOAI operator and Jupyter notebook, create the S3 bucket, and then run the model.
Running and Deploying LLMs using Red Hat OpenShift AI on ROSA cluster and Storing the Model in Amazon S3 Bucket
1. Introduction
Large Language Models (LLMs) are a specific type of generative AI focused on processing and generating human language. They can understand, generate, and manipulate human language in response to various tasks and prompts.
This guide is a simple example on how to run and deploy LLMs on a Red Hat OpenShift Services on AWS (ROSA) cluster, which is our managed service OpenShift platform on AWS, using Red Hat OpenShift AI (RHOAI) , which is formerly called Red Hat OpenShift Data Science (RHODS) and is our OpenShift platform for managing the entire lifecycle of AI/ML projects. And we will utilize Amazon S3 bucket to store the model output. In essence, here we will first install RHOAI operator and Jupyter notebook, create the S3 bucket, and then run the model.
